Lymphoma

An Algorithmic Approach to Finding Degree-Doubling Nodes in Oriented Graphs

Seymour's Second Neighborhood Conjecture claims that there will always exist a node whose out-degree doubles in the square of an oriented graph. In this paper, we first present a novel data structure, GLOVER (Graph Level Order), which partitions nodes into a total ordering of containers. This data structure establishes a well-ordering on oriented graphs and allows for the construction of a decre...

Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics

Federated Graph Learning (FGL) has demonstrated the advantage of training a global Graph Neural Network (GNN) model across distributed clients using their local graph data. Unlike Euclidean data (\eg, images), graph data is composed of nodes and edges, where the overall node-edge connections determine the topological structure, and individual nodes along with their neighbors capture local node f...

FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis

Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be hi...

Mamba-based Deep Learning Approaches for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography

Study Objectives: We investigate Mamba-based deep learning approaches for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non...

Computing the Non-Dominated Flexible Skyline in Vertically Distributed Datasets with No Random Access

In today's data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale,...

AI-Powered Intracranial Hemorrhage Detection: A Co-Scale Convolutional Attention Model with Uncertainty-Based Fuzzy Integral Operator and Feature Screening

Intracranial hemorrhage (ICH) refers to the leakage or accumulation of blood within the skull, which occurs due to the rupture of blood vessels in o...

Can We Get Rid of Handcrafted Feature Extractors? SparseViT: Nonsemantics-Centered, Parameter-Efficient Image Manipulation Localization through Spare-Coding Transformer

Non-semantic features or semantic-agnostic features, which are irrelevant to image context but sensitive to image manipulations, are recognized as e...

AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques

Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressin...

BinSparX: Sparsified Binary Neural Networks for Reduced Hardware Non-Idealities in Xbar Arrays

Compute-in-memory (CiM)-based binary neural network (CiM-BNN) accelerators marry the benefits of CiM and ultra-low precision quantization, making th...

Intuitive Axial Augmentation Using Polar-Sine-Based Piecewise Distortion for Medical Slice-Wise Segmentation

Most data-driven models for medical image analysis rely on universal augmentations to improve accuracy. Experimental evidence has confirmed their ef...

Mitigating epidemic spread in complex networks based on deep reinforcement learning.

Complex networks are susceptible to contagious cascades, underscoring the urgency for effective epidemic mitigation strategies. While physical quarant...

Artificial Intelligence-Driven Precision Medicine: Multi-Omics and Spatial Multi-Omics Approaches in Diffuse Large B-Cell Lymphoma (DLBCL).

In this comprehensive review, we delve into the transformative role of artificial intelligence (AI) in refining the application of multi-omics and spa...

How chromatin interactions shed light on interpreting non-coding genomic variants: opportunities and future direc-tions

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent ...

Redundancy Is All You Need

The seminal work of Bencz\'ur and Karger demonstrated cut sparsifiers of near-linear size, with several applications throughout theoretical computer...

Learning Graph Filters for Structure-Function Coupling based Hub Node Identification

Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional network...

Gain Cell-Based Analog Content Addressable Memory for Dynamic Associative tasks in AI

Analog Content Addressable Memories (aCAMs) have proven useful for associative in-memory computing applications like Decision Trees, Finite State Ma...

Node-reconfiguring multilayer networks of human brain function

Functional brain network properties are heavily influenced by how the the network nodes are defined. A common approach uses Regions of Interest (ROI...

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies...

Can virtual staining for high-throughput screening generalize?

The large volume and variety of imaging data from high-throughput screening (HTS) in the pharmaceutical industry present an excellent resource for t...

Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease.

BACKGROUND: Due to similar clinical manifestations and imaging signs, differential diagnosis of primary intestinal lymphoma (PIL) and Crohn's disease ...